Systems and methods using reputation or influence scores in search queries

ABSTRACT

One or more processors determine reputation scores for one or more subjects based on connections. One or more processors use a plurality of citations, with each citation representing an expression of opinion or description by a subject on an object. One or more processors select a subset of citations for each object from the citations citing each object. The content of the citations matches one or more search terms for a search query. One or more processors assign citation scores to a subset of a plurality of objects. The citation scores indicate relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of the objects by the one or more subjects. The selection scores for an object are determined for each search query based on a subset of subjects citing the object.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. application Ser. No. 14/169,494 filed on Jan. 31, 2014, which is a continuation of U.S. application Ser. No. 12/628,791 filed on Dec. 1, 2009, now issued as U.S. Pat. No. 8,688,701, which is a non-provisional of U.S. Provisional Patent Application No. 61/200,664 filed on Dec. 1, 2008, and which is also a continuation-in-part of U.S. application Ser. No. 11/809,489 filed on Jun. 1, 2007, now issued as U.S. Pat. No. 7,831,536. All applications above are fully incorporated herein by reference for all purposes.

BACKGROUND

1. Field of the Invention

The present invention is directed to systems and methods for search queries, and more particularly to systems and methods that use reputation or influence scores in search queries.

2. Description of the Related Art

Knowledge is increasingly more germane to our exponentially expanding information-based society. Perfect knowledge is the ideal that participants seek to assist in decision making and for determining preferences, affinities, and dislikes. Practically, perfect knowledge about a given topic is virtually impossible to obtain unless the inquirer is the source of all of information about such topic (e.g., autobiographer). Armed with more information, decision makers are generally best positioned to select a choice that will lead to a desired outcome/result (e.g., which restaurant to go to for dinner). However, as more information is becoming readily available through various electronic communications modalities (e.g., the Internet), one is left to sift through what is amounting to a myriad of data to obtain relevant and, more importantly, trust worthy information to assist in decision making activities. Although there are various tools (e.g., search engines, community boards with various ratings), there lacks any indicia of personal trustworthiness (e.g., measure of the source's reputation and/or influence) with located data.

Currently, a person seeking to locate information to assist in a decision, to determine an affinity, and/or identify a dislike can leverage traditional non-electronic data sources (e.g., personal recommendations-which can be few and can be biased) and/or electronic data sources such as web sites, bulletin boards, blogs, and other sources to locate (sometimes rated) data about a particular topic/subject (e.g., where to stay when visiting San Francisco). Such an approach is time consuming and often unreliable as with most of the electronic data there lacks indicia of trustworthiness of the source of the information. Failing to find a plethora (or spot on) information from immediate non-electronic and/or electronic data source(s), the person making the inquiry is left to make the decision using limited information, which can lead to less than perfect predictions of outcomes, results, and can lead to low levels of satisfaction undertaking one or more activities for which information was sought.

Current practices also do not leverage trustworthiness of information or, stated differently, attribute a value to the reputation of the source of data (e.g., referral). With current practices, the entity seeking the data must make a value judgment on the reputation of the data source. Such value judgment is generally based on previous experiences with the data source (e.g., rely on Mike's restaurant recommendations as he is a chef and Laura's hotel recommendations in Europe as she lived and worked in Europe for 5 years). Unless the person making the inquiry has an extensive network of references from which to rely to obtain desired data needed to make a decision, most often, the person making the decision is left to take a risk or “roll the dice” based on best available non-attributed (non-reputed) data. Such a prospect often leads certain participants from not engaging in a contemplated activity.

Reputation accrued by persons in such a network of references is subjective. In other words, reputation accrued by persons in such a network of references appear differently to each other person in the network, as each person's opinion is formed by their own individual networks of trust.

Real world trust networks follow a small-world pattern, that is, where everyone is not connected to everyone else directly, but most people are connected to most other people through a relatively small number of intermediaries or “connectors”. Accordingly, this means that some individuals within the network may disproportionately influence the opinion held by other individuals. In other words, some people's opinions may be more influential than other people's opinions.

Influence has been provided for augmenting reputation, which may be subjective. In some embodiments, influence is provided as an objective measure. For example, influence can be useful in filtering opinions, information, and data. Although reputation and influence provide advantages in accordance with some embodiments for the ranking of individuals or products or services of any type in any means or form, there are some drawbacks and limitations.

There is a need for improved systems and methods that use reputation or influence scores in search queries . . . .

SUMMARY

An object of the present invention is to provide improved systems and methods that enable a person to obtain information useful in search queries.

There is a further need to provide improved systems and methods that use reputation or influence scores in search queries.

These and other objects of the present invention are achieved in, a system of ranking entities using reputation or influence scores. One or more processors determine reputation scores for one or more subjects based on connections. One or more processors use a plurality of citations, with each citation representing an expression of opinion or description by a subject on an object. One or more processors select a subset of citations for each object from the citations citing each object. The content of the citations in the selected subset matches one or more of search terms for a search query. One or more processors assign citation scores to a subset of a plurality of objects. The citation scores indicate relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of the objects by the one or more subjects. The selection scores for an object are determined for each search query based on a subset of subjects citing the object. One or more processors combine the citation scores for the objects and the reputation scores for the subjects, citing the objects to calculate selection scores for the objects determined based on matching of the one or more search terms with the content of the citations. The selection scores for an object determine, for each search query, are based on a subset of subjects citing the object. One or more processors select and rank the objects based on the selection scores of the objects. A different ranking is computed for a same set or overlapping sets of objects when the search query is different.

In another embodiment of the present invention, a method of ranking entities uses reputation or influence scores. A processor is used to determine reputation scores for one or more subjects based on connections. A plurality of citations for provided. Each citation represents an expression of opinion or description by a subject on an object. A processor is used to select a subset of citations for each object from the citations citing each object. The content of the citations in the selected subset matches one or more of search terms for a search query. Citation scores are assigned to a subset of a plurality of objects. The citation scores indicate relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of the objects by the one or more subjects. The selection scores for an object are determined for each search query based on a subset of subjects citing the object. The citation scores for the objects and the reputation scores are combined for the subjects citing the objects to calculate selection scores for the objects determined based on matching of the one or more search terms with the content of the citations. The selection scores for an object are determined for each search query based on a subset of subjects citing the object, with the subjects in the subset being the subjects of previously selected subsets of citations to each object. A different selection score is computed for the same object when a different search query is provided. Objects are ranked based on the selection scores of the objects. A different ranking is computed for a same set or overlapping sets of objects when the search query is different.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustrative model of the World Wide Web showing individuals linked to other individuals and to documents they author.

FIG. 2 is a block diagram showing the cooperation of exemplary components of another illustrative implementation in accordance with some embodiments.

FIG. 3 is a block diagram showing an illustrative block representation of an illustrative system in accordance with some embodiments.

FIG. 4 is a block diagram describing the interaction of various parties of an exemplary referral environment in accordance with some embodiments.

FIG. 5 is a block diagram of the search space of an exemplary referral environment in accordance with some embodiments.

FIG. 6 is a flow diagram showing illustrative processing performed in generating referrals in accordance with some embodiments.

DETAILED DESCRIPTION

It will be appreciated that the systems and methods of the present invention can be implemented in numerous ways, including as a process; an apparatus; a system; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.

The present invention provides improved systems and methods that use reputation or influence scores in search queries and decision making. In one embodiment, systems and methods are provided that allow for the use of reputation scores and/or influence scores to determine at least in part, and/or in combination with other methods and systems, the ranking of any subset of individual entities in a given set of entities. As a non-limiting example, the he entities can include any of the following: documents on the Internet, products, services, data files, legal or natural persons, or any entities in any form or means that can be represented within the computer network. The systems and methods described herein in accordance with some embodiments provide that a subset of the ranked entities are made available based on selection criteria, such as rank, date or time, geography/location associated with the entity, and/or any other selection criteria. The systems and methods described herein in accordance with some embodiments provide that the influence and/or reputation are estimated using any technique, including but not limited to the various techniques described herein.

The systems and methods described herein in accordance with some embodiments provide for the use of reputation scores and/or influence scores to determine at least in part, in combination with other methods and systems, the ranking of any subset of individual entities in a given set of entities; in which the entities include natural or legal persons, or other entities such as computational processes, documents, data files, or any form of product or service or information of any means or form for which a representation has been made. In some embodiments, the measures of influence and reputation are on dimensions that, for example, are related to a specific topic (e.g., automobiles or restaurants), or source (e.g., a weblog or Wikipedia entry or news article or Twitter feed), or search term (e.g., key words or phrases specified in order to define a subset of all entities that match the search term(s)), in which a subset of the ranked entities are made available based on selection criteria, such as the rank, date or time, or geography/location associated with the entity, and/or any other selection criteria.

An illustrative implementation of systems and methods described herein in accordance with some embodiments includes a social graph of individuals on the Internet, as shown in FIG. 1, in which the individuals represent natural or legal persons and the documents (e.g., #1 and #2) represent natural or legal persons, or other entities such as computational processes, documents, data files, or any form of product or service or information of any means or form for which a representation has been made within the computer network within this system.

In some embodiments, the social graph is directed (e.g., a directed graph). In some embodiments, the social graph is undirected (e.g., an undirected graph).

In some embodiments, the social graph is explicit, with individuals expressing a link to other individuals. In some embodiments, the social graph is implicit, with techniques for identifying the links between individuals, such as trust, respect, positive, or negative opinion.

In some embodiments, the links or edges of the social graph represent different forms of association including, for example, friendship, trust, acquaintance and the edges on the graph are constrained by dimensions representing ad-hoc types including but not limited to subjects, fields of interest, and/or search terms.

In some embodiments, the nodes of the social graph represent people or other entities (e.g., web pages; authors; reviewers; users of micro blogging services, such as Twitter; users of social networks, such as MySpace or Facebook; bloggers; and/or any other entity) that include expressions of opinion, reviews, or other information useful for the estimation of influence, and that each node on the graph represents an influential entity, once influence for that node has been, for example, estimated.

In some embodiments, the nodes are placed in two categories, as subjects capable of having an opinion or making a citation, in which such expression is explicit, expressed, implicit, or imputed through any other technique; and as objects about which subjects have opinions or make citations; in which, for example, certain objects are also subjects; and in which an object has reputation scores indicating the possibly collective opinion of subjects on the object, and subjects have influence scores indicating the degree to which the subjects opinion influences other subjects.

In some embodiments, in which nodes are in two categories as described above, the reputation scores of objects are uses for the selection and ranking of objects, for example, weighted by the influence scores of the subjects related to the object, for example, in combination with other attributes of objects including to semantic or descriptive data regarding the object. In some embodiments, the subjects include one or more of the following: users of micro blogging services such as Twitter, users of social networks such as MySpace or Facebook, bloggers, and reviewers. In some embodiments, the objects include one or more of the following: Internet web sites, blogs, music, videos, books, films, and other objects that can be represented with, for example, a Uniform Resource Identifier (URI). In some embodiments, the objects include entities that are each associated with a Uniform Resource Identifier (URI), wherein the subjects include entities representing authors of Internet content or users of social media services including one or more of the following: blogs, Twitter, and reviews on Internet web sites, wherein the links are citations of objects from subjects including one or more of the following: Tweets, blog posts, and reviews of objects on Internet web sites.

FIG. 2 is a block diagram showing the cooperation of exemplary components of another illustrative implementation in accordance with some embodiments. In particular, FIG. 2 shows an illustrative implementation of exemplary reputation attribution platform 200 in accordance with some embodiments. As shown in FIG. 2, exemplary reputation attribution platform 200 includes client computing environment 220, client computing environment 225 up to and including client computing environment 230, communications network 235, server computing environment 260, intelligent reputation engine 250, verification data 240, community data 242, reputation guidelines 245, and reputation histories data 247. Also, as shown in FIG. 2, exemplary reputation attribution platform 200 includes a plurality of reputation data (e.g., inputted and/or generated reputation data) 205, 210, and 215 which can be displayed, viewed, stored, electronically transmitted, navigated, manipulated, stored, and printed from client computing environments 220, 225, and 230, respectively.

In some embodiments, in an illustrative operation, client computing environments 220, 225, and 230 can communicate and cooperate with server computing environment 260 over communications network 235 to provide requests for and receive reputation data 205, 210, and 215. In the illustrative operation, intelligent reputation engine 250 can operate on server computing environment 260 to provide one or more instructions to server computing environment 260 to process requests for reputation data 205, 210, and 215 and to electronically communicate reputation data 205, 210, and 215 to the requesting client computing environment (e.g., client computing environment 220, client computing environment 225, or client computing environment 230). As part of processing requests for reputation data 205, 210, and 215, intelligent reputation engine 250 can utilize a plurality of data comprising verification data 240, community data 242, reputation guidelines 245, and/or reputation histories data 247. Also, as shown in FIG. 2, client computing environments 220, 225, and 230 are capable of processing content production/sharing data 205, 210, and 215 for display and interaction to one or more participating users (not shown).

FIG. 3 is a block diagram showing an illustrative block representation of an illustrative system in accordance with some embodiments. In particular, FIG. 3 shows a detailed illustrative implementation of exemplary reputation attribution environment 300 in accordance with some embodiments. As shown in FIG. 3, exemplary content reputation attribution environment 300 includes intelligent reputation platform 320, verification data store 315, reputation guidelines data store 310, reputation histories data store 305, community data store 307, user computing environment 325, reputation targets (e.g., users) 330, community computing environment 340, and community 345. Additionally, as shown in FIG. 3, reputation attribution environment 300 includes reputation session content 350, which can be displayed, viewed, transmitted and/or printed from user computing environment 325 and/or community computing environment 340.

In some embodiments, in an illustrative implementation, intelligent reputation platform 320 can be electronically coupled to user computing environment 325 and community computing environment 340 via communications network 335. In some embodiments, communications network 335 includes fixed-wire (e.g., wire line) and/or wireless intranets, extranets, and/or the Internet.

In some embodiments, in an illustrative operation, users 330 can interact with a reputation data interface (not shown) operating on user computing environment 325 to provide requests to initiate a reputation session that are passed across communications network 335 to intelligent reputation platform 320. In the illustrative operation, intelligent reputation platform 320 can process requests for a reputation session and cooperate with interactive verification data store 315, reputation guidelines data store 310, reputation histories data store 305, and community data store 307 to generate a reputation session for use by users 330 and community 345.

In some embodiments, in an illustrative implementation, verification data store 315 can include data representative of connections between users 330 and community members 345. Such data can include but is not limited to connections between users to identify a degree of association for use in generation of reputation data. In the illustrative implementation, reputation guideline data store 310 can include data representative of one or more rules for attributing reputations amongst users 330 and community 345. Reputation histories data store 305 can include one or more generated reputation attributions for use as part of reputation data processing. Community data store 307 can include data representative of community feedback for generated reputation data. For example, the data representative of connections can be provided through user input or generated from any number of techniques including but not limited to automated or computer-assisted processing of data available on computer networks, links expressed or implied between entities on social networking websites, user commentary or “blogging” websites, or any other form of document available on the Internet.

FIG. 4 is a block diagram describing the interaction of various parties of an exemplary referral environment in accordance with some embodiments. In particular, FIG. 4 shows contributing elements of exemplary reputation attribution environment 400 in accordance with some embodiments. As shown, exemplary reputation attribution environment 400 comprises a plurality of sub-environments 405, 410, and 415 and numerous reputation targets A-Q. As shown, reputation targets can have direct and/or indirect connections with other reputations targets within a given sub-environment 405, 410, or 415 and/or with other reputation targets that are outside sub-environments 405, 410, 415.

In some embodiments, in an illustrative implementation, sub-environments 405, 410, or 415 can represent one or more facets of a reputation target's experience, such as work, home, school, dub(s), and/or church/temple/commune. In the illustrative implementation, an exemplary reputation target Q can inquire about the reputation of other reputation targets (e.g., obtain trusted data for use to assist in making a decision, determine an affinity, and/or identify a dislike). The individual reputations of each of the target participants can be derived according to the herein described techniques (e.g., in FIGS. 5 and 6) so that each reputation target is attributed one or more reputation indicators (e.g., a reputation score associated for restaurant referrals, another reputation score associated for movie referrals, another reputation score associated for match-making, etc.). The reputation indicators can be calculated based on the degree and number of relationships between reputation targets in a given sub-environment and/or outside of a sub-environment. Once calculated, an exemplary reputation target Q can query other reputation targets for trusted data (e.g., recommendations and/or referrals) and can process such trusted data according to reputation score of the data source (e.g., reputation target).

For example, sub-environment 405 can represent a place of business, sub-environment 410 can represent home, and sub-environment can represent a country club. In some embodiments, in an illustrative operation, each of the reputation targets of reputation attribution environment 400 can be attributed one or more reputation scores (e.g., reputation score for business data, reputation score for family data, etc.). In the illustrative operation, the reputation score for each reputation target for each category (e.g., business, family, social, religious, etc.) can be calculated according to the degree of relationship with other reputation targets and/or the number of connections with other relationship targets.

In some embodiments, in the illustrative operation, reputation target a can request data regarding a business problem (e.g., how to broker a transaction). Responsive to the request, the reputation targets of sub-environment 405 (e.g., reputation target can act as data sources for reputation target Q) providing data that can satisfy reputation target Q's request. Additionally, other reputation targets, who are not directly part of sub-environment 405, can also act as data sources to reputation target Q. In this context, the reputation score for reputation targets A, B, C, and/or D) can have a higher reputation score than other reputation targets not part of sub-environment 405 as such reputation targets are within sub-environment 405, which is focused on business. In the illustrative operation, other reputation targets not part of sub-environment 405 can have equal or near level reputation scores to reputation targets (A, B, C, and/or D) of sub-environment 405 based on the connections with reputation targets A, B, C, and/or D and reputation target Q. For example, as shown in FIG. 4, reputation target I can have a relatively high reputation score as it pertains to business as reputation target I has a number of direct and indirect connections (I-A, I-G-B, I-H-D, I-G-E-D) to reputation targets (e.g., A, B, C, and/or D) of sub-environment 405 and to inquiring reputation target Q.

It is appreciated that although exemplary reputation attribution environment 400 of FIG. 4 is shown have a configuration of sub-environments having various participants, that such description is merely illustrative the contemplated reputation attribution environment of the herein described systems and methods can have numerous sub-environments with various participants in various non-described configurations.

FIG. 5 is a block diagram of the search space of an exemplary referral environment in accordance with some embodiments. In particular, FIG. 5 shows exemplary reputation scoring environment 500 in accordance with some embodiments. As shown in FIG. 5, reputation scoring environment 500 includes a plurality of dimensions 505, 510, and 515, which are operatively coupled to one or more transitive dimensions 520 and 525. Further, as shown, reputation scoring environment 500 includes one or more entities 530, 535, 545, 550, 560, and 570 residing on one or more of dimensions 505, 510, and 515 as well as transitive connectors 540, 565, 570, and 580 residing on transitive dimensions 520 and 525.

In some embodiments, in an illustrative operation, scores for one or more entities 530, 535, 545, 550, 560 and/or 570 can be determined on a network (not shown) on a given dimension 505, 510 and/or 515. In the illustrative operation, an entity 530, 535, 545, 550, 560 and/or 570 can be directly linked to any number of other entities 530, 535, 545, 550, 560 and/or 570 on any number of dimensions 505, 510, and/or 515 (e.g., such that each link, direct or indirect link, can be associated with a score). For example, one or more dimension 505, 510, and/or 515 can have an associated one or more transitive dimension 520 and/or 525.

In the illustrative operation, a directed path 507 on a given dimension 505 between two entities 530 and 535, a source and a target, includes a directed link from the source entity 530 (e.g., illustratively 530 as all entities 530, 535, 545, 550, 560, and/or 570 can be source and/or target entities depending on the perspective of the scoring attribution platform as described herein in accordance with various embodiments) to an intermediate entity 540, prefixed to a directed path from the intermediate entity 540 to the target entity 535.

In some embodiments, in an illustrative implementation, links on the path can be on one or more transitive dimensions 520 and/or 525 associated with a given dimension 505, 510, and/or 515. For example, to determine a score on a given dimension 505, 510, and/or 515 between a source entity 530 and a target entity 535, directed paths 507 on the given dimension 505, 510, and/or 515 can be determined through any kind of graph search (not shown). In the illustrative operation, the individual scores on the one or more links on the one or more paths can be combined to produce one or more resulting scores using various techniques for propagating scores and for resolving conflicts between different scores. For example, one or more intermediate entities 540, 565, 570, and/or 580 can also be provided with a measure of influence on the dimensions 505, 510 and/or 515 based on the universe of source entities (e.g., 530, 535, 545, 550, 560, 570), the universe of target entities (e.g., 530, 535, 545, 550, 560, 570) and the links between them.

It is appreciated that although reputation scoring environment 500 is shown to have a particular configuration operating to an illustrative operation with a particular number of dimensions, transitive dimensions, entities, direct connections and indirect connections that such description is merely illustrative as the influence calculation within the herein described techniques can employ various dimensions, transitive dimensions, entities, direct, and/or indirect connections having various configurations and assemblages operating according to other illustrative operations.

FIG. 6 is a flow diagram showing illustrative processing performed in generating referrals in accordance with some embodiments. In particular, FIG. 6 shows exemplary processing in calculating reputations scores in accordance with some embodiments. As shown in FIG. 6, processing begins at block 600 at which a population of entities are identified. From there processing proceeds to block 605 at which selected constraints are established on the identified population such that the interrelationships between the entities can be mapped to values −1 to +1 for a target entity connected to source entity. Processing then proceeds to block 610 at which entity relationships are represented as a directed graph on a given dimension such that an entity can be directly, uni-directionally linked to any number of other entities on any number of dimensions with each direct link having an associated score within a selected range R such that each dimension can have therewith an associated transitive dimension. From there, processing proceeds to block 615 at which a graph search is performed to identify directed paths from a source entity to a target entity on a given dimension to generate a global directed graph having combinations of available identified directed paths and to generate a scoring graph for identified directed paths. Processing then proceeds to block 620 at which individual scores of the direct links on an identified path can be combined to generate one or more final scores (e.g., reputation score) for a target entity from the perspective of a source entity.

In some embodiments, in an illustrative implementation, the processing of FIG. 6 can be performed such that for a population of entities, a method of determining scores, each within the range R which can be mapped to the values −1 . . . +1, for a target entity connected to a source entity on a network that can be conceptually represented as a directed graph on each given dimension, such that an entity can be directly, uni-directionally linked to any number of other entities on any number of dimensions, with each direct link having an associated score within the range R. Further, each dimension can have an associated transitive dimension and such that a directed path on a given dimension between two entities, a source entity and a target entity, can be defined as a direct link from the source entity to an intermediate entity, prefixed to a directed path from the intermediate entity to the target entity, subject to the selected constraints including but not limited to: 1) a direct link from any entity to the target entity must be on the given dimension, and 2) a direct link on the path from any entity to an intermediate entity that is not the target entity must be either on the transitive dimension associated with the given dimension, or on the given dimension itself if the given dimension is itself is a transitive dimension.

Furthermore, in the illustrative operation, the processing of FIG. 6 can include but is not limited to: (A) performing a graph search (e.g., using various graph search techniques) to identify directed paths from a source entity to a target entity on a given dimension subject to the above definition of a directed path that, for example, optimally results in a directed graph combining all such identified directed paths. The resulting directed graph, for example, provides a scoring graph that can be stored separately. In the illustrative operation, individual scores can be combined (B) on each direct link on each path on the scoring graph to produce one or more final scores, with or without an associated set of confidence values in the range C=0 . . . 1 for each resulting score, for the target entity from the perspective of the source entity. In the illustrative operation, the acts (A) and (B) can be performed, for example, in sequence, or performed simultaneously; when performed simultaneously, the combination of Individual scores described in act (B) being performed during the graph search described in act (A) without the creation of separately stored scoring graph; and wherein the graph search performed in act (A) can be optimized by some combination of scores identified through act (B) such that the optimization may result in the exclusion of certain paths between the source entity and the target entity.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can, for example, be estimated as the count of other entities with direct links to the entity or with a path, possibly with a predefined maximum length, to the entity; with or without the count being adjusted by the possible weights on each link, the length of each path, and the level of each entity on each path.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can be estimated with the adjusted count calculated through the operations described herein, transformed into a rank or percentile relative to the similarly measured influence of all other entities.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can be estimated as the count of actual requests for data, opinion, or searches relating to or originating from other entities, entities with direct links to the entity or with a path, possibly with a predefined maximum length, to the entity; such actual requests being counted if they result in the use of the paths originating from the entity (e.g., representing opinions, reviews, citations or other forms of expression) with or without the count being adjusted by the possible weights on each link, the length of each path, and the level of each entity on each path.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can be estimated as the count of actual requests for data, opinion, or searches relating to or originating from other entities, entities with direct links to the entity or with a path, possibly with a predefined maximum length, to the entity; such actual requests being counted if they occur within a predefined period of time and result in the use of the paths originating from the entity (e.g., representing opinions, reviews, citations or other forms of expression) with or without the count being adjusted by the possible weights on each link, the length of each path, and the level of each entity on each path.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can be estimated with the adjusted count calculated through the operations described herein, transformed into a rank or percentile relative to the similarly measured influence of all other entities.

In some embodiments, in an Illustrative operation of the herein described techniques using various systems and methods, the influence of each entity can be estimated by applying to it any of several graph metric functions, such as centrality or betweenness, in which the functions, such as centrality or betweenness, can be estimated either by relating the entity to the entire graph including all linked entities, or by relating the entity to a sub graph including all entities linked to the entities directly or by paths of up to a given length.

In some embodiments, the illustrative operations described herein for the calculation of influence can be performed for each dimension separately, resulting in one influence measure for each entity for each dimension; for all dimensions together, resulting in one influence measure for each entity; or for any given subgroup of dimensions together applied to any given entity, resulting in each entity having as many influence measures as the number of subgroups of dimensions applied to that entity.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the influence of each entity as estimated in each of the operations described herein, can be adjusted by metrics relating to the graph comprising all entities or a subset of all linked entities. For example, such metrics can include the density of the graph, defined as the ratio of the number of links to the number of linked entities in the graph; such metrics can be transformed by mathematical functions optimal to the topology of the graph, especially where it is known that the distribution of links among entities in a given graph may be non-linear. An example of such an adjustment would be the operation of estimating the influence of an entity as the number of directed links connecting to the entity, divided by the logarithm of the density of the graph comprising all linked entities. Such an operation can provide an optimal method of estimating influence rapidly with a limited degree of computational complexity.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, in which the influence of entities as estimated in each of the operations described herein can be estimated for separate, unconnected graphs; and for which such influence estimated for entities in separate, unconnected graphs can be adjusted by applying metrics relating to each separate unconnected graph in its entirety, as shown in the operation described herein; the influence of each entity on one graph, thus adjusted, can be normalized and compared to the influence of another entity on another graph, also thus adjusted. For example, such an operation allows for the use of influence measures across separate, unconnected graphs.

In some embodiments, in an illustrative operation of the herein described techniques using various systems and methods, the estimation of influence can be optimized for different contexts and requirements of performance, memory, graph topology, number of entities, etc., by any combination of the operations described above in paragraphs above, and any similar operations involving metrics including but not limited to values comprising: the number of potential source entities to the entity for which influence is to be estimated, the number of potential target entities, the number of potential directed paths between any one entity and any other entity on any or all given dimensions, the number of potential directed paths that include the entity, the number of times within a defined period that a directed link from the entity is used for a scoring, search or other operation.

In some embodiments, in an illustrative implementation of this system applied to the World Wide Web and documents, data and information on the Internet, such data and information are modeled (as shown in FIG. 1) as documents, or objects, and authors, or subjects, in which subjects are representation of any entities that make citations, in which citations include the expression of opinions on other subjects or objects, expressions of authors in the form of postings to blogs, Wikipedia entries, postings to social media such as Twitter or Jaiku, postings to websites, postings in the form of reviews, recommendations, or any other form of citation made to mailing lists, newsgroups, discussion forums, comments to websites or any other form of Internet publication; in which citations by one subject regarding another subject, such as a recommendation of one author by another, is treated as representing an expression of trustworthiness, for example, limited to category or categories identified or imputed, and citations by one subject regarding an object, such as a recommendation of a website, or a restaurant review, is treated as representation an expression of opinion or description; in which citations are treated as edges on a network where subjects and objects form nodes; and where reputation is determined for subjects and objects and influence is determined for subjects and where objects are be identified, selected and ranked based on the reputation ascribed to them or the influence of subjects citing objects or the date of the citations or the content of citations matching search terms or the content of documents matching search terms, or any computation of rank and selection based on a function of any combination of these variables and additional variables.

In some embodiments, in an illustrative implementation of the techniques described herein is implemented as follows; in which users can provide text, referred to here as “search terms”, and for every object represented, such as by a Uniform Resource Locator (“URL”), calculations are made for all represented citations of that object in order to determine semantic weights such as term frequency-inverse document frequency (“TF-IDF”), as well as the influence of the author of each such citation of each object, and a function of the semantic weight and influence of all citations of each object results in a score for the object on the basis of which a ranking of objects is performed and a ranked list of objects is presented to the user; and where the score is used as a threshold for the selection of objects, and where other ranking criteria (e.g., first, last, modal, median date of citations of the object) is used in some combination with the score.

In some embodiments, in an illustrative implementation of the techniques described herein provides for the selection score and object ranking as a combination of not only the citation score for objects and the influence score for subjects citing them, but an expertise score for each subject citing each such object, based on the citations from each subject matching descriptive criteria (“search terms” or ontologically similar terms) as a relative share of all citations from that subject, and citations from all subjects matching above-described descriptive criteria as a relative share of citations from all subjects.

In some embodiments, in an illustrative implementation of the techniques described herein provides for the selection score and object ranking as a combination of not only the citation score for objects and the reputation score for subjects citing them, but an expertise score for each subject citing each such object, based on the citations from each subject matching descriptive criteria (“search terms” or ontologically similar terms) as a relative share of all citations from that subject, and citations from all subjects matching above-described descriptive criteria as a relative share of citations from all subjects.

It is understood that the herein described techniques using various systems and methods are susceptible to various modifications and alternative constructions. There is no intention to limit the herein described systems and methods to the specific constructions described herein. On the contrary, the herein described systems and methods are intended to cover all modifications, alternative constructions, and equivalents falling within the scope and spirit of the herein described techniques using, for example, various systems and methods.

It should also be noted that the herein described systems and methods can be implemented in a variety of electronic environments (including both non-wireless and wireless computer environments, including cell phones and video phones), partial computing environments, and real world environments. The various techniques described herein may be implemented in hardware or software, or a combination of both. In some embodiments, the techniques are implemented in computing environments maintaining programmable computers that include a computer network, processor, servers, and a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Computing hardware logic cooperating with various instructions sets are applied to data to perform the functions described above and to generate output information. The output information is applied to one or more output devices. For example, programs used by the exemplary computing hardware can be implemented in various programming languages, including high level procedural or object oriented programming language to communicate with a computer system. Illustratively the herein described techniques using various apparatus and methods may be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. In some embodiments, each such computer program can be stored on a storage medium or device (e.g., ROM or magnetic disk) that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer to perform the procedures described above. In some embodiments, the apparatus may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner.

Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive. 

1-20. (canceled)
 21. A non-transitory machine-readable medium containing executable program instructions which when executed by a data processing device cause the device to perform a method of ranking objects using reputation scores, comprising: determining reputation scores for one or more subjects based on connections, the reputation scores indicating reputations of the subjects and a subject is a representation of a user of a social network; receiving a plurality of citations, each citation representing an online posting in the social network of an expression of opinion by a subject on an object, wherein the object is an object that is accessible outside of the social network; selecting a subset of citations for each object from the citations citing each object, the content of the citations in the selected subset matching one or more of search terms of a search query; assigning citation scores to a subset of a plurality of objects, the citation scores indicating relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of these objects by the one or more subjects; determining a selection score for each of the subset of a plurality of objects based on the citation scores for these objects and the reputation scores for the subjects citing these objects; and selecting and ranking the objects based on the selection scores of the objects.
 22. The machine-readable medium of claim 21, wherein a different subset of citations being selectable for a same object when a different search query is provided.
 23. The machine-readable medium of claim 21, wherein the subjects in the subset of subjects are the subjects of previously selected subsets of citations to each object.
 24. The machine-readable medium recited in claim 21, wherein the objects include books, films, music, documents, websites, objects for sale, objects that are reviewed or recommended or cited, or any entities that are associated with a Uniform Resource Identifier (URI), wherein the subjects include entities representing authors of Internet content or users of social media services.
 25. The machine-readable medium recited in claim 21, wherein the method further comprises: receiving descriptive criteria from a user.
 26. The machine-readable medium recited in claim 21, wherein the method further comprises: receiving search terms, wherein the search terms are provided in a search query, and wherein the search terms are included in the descriptive criteria.
 27. The machine-readable medium recited in claim 21, wherein the method further comprises: displaying a subset of top ranked objects based on the selection scores.
 28. The machine-readable medium recited in claim 21, wherein the method further comprises: receiving search terms, wherein the search terms are provided in a search query; and displaying a subset of top ranked objects based on the selection scores, wherein the top ranked objects based on the selection scores provide a subjective based search result.
 29. The machine-readable medium recited in claim 21, wherein the method further comprises: determining a content score for an object based on the description criteria; wherein the selection score is based on the citation score, the subjective reputation score, and the content score.
 30. The machine-readable medium recited in claim 21, wherein the method further comprises: determining an expertise score for each subject citing each object based on a second descriptive criteria, wherein the selection score is based on the citation score, the subjective reputation score, and the expertise score.
 31. A method of ranking objects using reputation scores, comprising: determining reputation scores for one or more subjects based on connections, the reputation scores indicating reputations of the subjects and a subject is a representation of a user of a social network; receiving, with one or more processors, a plurality of citations, each citation representing an online posting in the social network of an expression of opinion by a subject on an object, wherein the object is an object that is accessible outside of the social network; selecting a subset of citations for each object from the citations citing each object, the content of the citations in the selected subset matching one or more of search terms of a search query; assigning citation scores to a subset of a plurality of objects, the citation scores indicating relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of these objects by the one or more subjects; determining a selection score for each of the subset of a plurality of objects based on the citation scores for these objects and the reputation scores for the subjects citing these objects; and selecting and ranking the objects based on the selection scores of the objects.
 32. The method of claim 31, wherein a different subset of citations being selectable for a same object when a different search query is provided.
 33. The method of claim 31, wherein the subjects in the subset of subjects are the subjects of previously selected subsets of citations to each object.
 34. The method of claim 31, wherein the objects include books, films, music, documents, websites, objects for sale, objects that are reviewed or recommended or cited, or any entities that are associated with a Uniform Resource Identifier (URI), wherein the subjects include entities representing authors of Internet content or users of social media services.
 35. The method of claim 31, further comprising: receiving descriptive criteria from a user.
 36. The method of claim 31, further comprising: receiving search terms, wherein the search terms are provided in a search query, and wherein the search terms are included in the descriptive criteria.
 37. The method of claim 31, further comprising: displaying a subset of top ranked objects based on the selection scores.
 38. The method of claim 31, further comprising: determining a content score for an object based on the description criteria; wherein the selection score is based on the citation score, the subjective reputation score, and the content score.
 39. The method of claim 11, further comprising: determining an expertise score for each subject citing each object based on a second descriptive criteria, wherein the selection score is based on the citation score, the subjective reputation score, and the expertise score.
 40. A device that ranks objects using reputation scores, the device comprising: a processor; a memory coupled to the processor though a bus; and a process executed from the memory by the processor causes the processor to determine reputation scores for one or more subjects based on connections, the reputation scores indicating reputations of the subjects and a subject is a representation of a user of a social network, receive, with one or more processors, a plurality of citations, each citation representing an online posting in the social network of an expression of opinion by a subject on an object, wherein the object is an object that is accessible outside of the social network, select a subset of citations for each object from the citations citing each object, the content of the citations in the selected subset matching one or more of search terms of a search query, assign citation scores to a subset of a plurality of objects, the citation scores indicating relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of these objects by the one or more subjects, determine a selection score for each of the subset of a plurality of objects based on the citation scores for these objects and the reputation scores for the subjects citing these objects, and select and ranking the objects based on the selection scores of the objects. 